1. Estimating currents and electric fields in the high-latitude ionosphere using ground- and space-based observations Ellen Cousins 1, Tomoko Matsuo 2,3, Art Richmond 1 1 NCAR-HAO, 2 CU-CIRES, 3 NOAA-SWPC FESD-ECCWES Meeting – 10 Feb 20141/13 J || ΣpΣp Φ

2. High-latitude Ionospheric Currents  Currents from magnetosphere close through high-latitude ionosphere  Drive currents parallel to and perpendicular to ionospheric electric field (Pedersen & Hall currents) E E E E  Satellites sample magnetic perturbations (  field-aligned currents)  SuperDARN radars sample plasma drifts (  electric fields)  Goal: Combine the two data sets and estimate complete (2D) current & electric field distribution FESD-ECCWES Meeting – 10 Feb 20142/13

3. [Brian Anderson] Active Magnetosphere and Planetary Electrodynamics Response Experiment AMPERE: StandardAMPERE: High ~1° lat. res.~ 0.1° lat. res. 3FESD-ECCWES Meeting – 10 Feb 2014 Magnetometer on every satellite 6 orbit planes (12 cuts in local time) ~11 satellites/plane 9 minute spacing - re-sampling cadence 780 km altitude, circular, polar orbits Iridium for Science

4. Using observations of Inverse procedure to infer maps of Assimilative Mapping of Ionospheric Electrodynamics [Richmond and Kamide, 1988] Linear relationships (for a given Σ) Given 2 of E, Σ, ΔB, can in theory solve for remaining variables FESD-ECCWES Meeting – 10 Feb 20144/13 Electric field (from SuperDARN) Conductance (height-integrated conductivity) – tensor (no observations for this study) Magnetic pertubations (from AMPERE) Ionospheric current density (no observations for this study) - Electrostatic potential - Field aligned current density ()

5. x a – analysis y – observations x b – background model H – forward operator K – Kalman gain P b – background model error covariance R – observational error covariance  Use the optimal interpolation (OI) method of data assimilation  Optimally combine information from observations and a background model, taking into account error properties of both x b yx b x a = x b + K (y – H x b ) K = P b H T (H P b H T + R) -1 FESD-ECCWES Meeting – 10 Feb 20145/13 Assimilative Mapping Procedure [From EOF] [analysis] [physics + Σ]

6.  Use the optimal interpolation (OI) method of data assimilation  Optimally combine information from observations and a background model, taking into account error properties of both  Background model and its error properties (from EOF analysis) previously determined for SuperDARN data  Recently did similar analysis for AMPERE data  But only have 1 week of data (used years for SuperDARN analysis)  Data quality issues FESD-ECCWES Meeting – 10 Feb 20146/13 Assimilative Mapping Procedure

7. Calculated using just across-track component of ΔB EOF 2 mean EOF 1 EOF 5 EOF 3 EOF 4 EOF 2 mean EOF 1 EOF 5 EOF 3 EOF 4 Calculated using just along-track component of ΔB Relative contribution of mean and each EOF to total observed ΔB 2 (more flat spectrum) (more peaked spectrum) FESD-ECCWES Meeting – 10 Feb 20147/13 AMPERE EOFs

8. x a – analysis y – observations x b – background model H – forward operator K – Kalman gain P b – background model error covariance R – observational error covariance  Use the optimal interpolation (OI) method of data assimilation  Optimally combine information from observations and a background model, taking into account error properties of both x b yx b x a = x b + K (y – H x b ) K = P b H T (H P b H T + R) -1 FESD-ECCWES Meeting – 10 Feb 20148/13 [From EOF] [analysis] [physics + Σ] Assimilative Mapping Procedure

9. FESD-ECCWES Meeting – 10 Feb 20149/13 Ionospheric Conductance  Height-integrated conductivity (tensor)  Assumed infinite along magnetic field lines  Pederson/Hall conductance || / to E  Solar-produced component  Empirical model – assumed to be reasonably accurate  Auroral component unknown  Highly variable in space and time  Estimate using empirical model  Could adjust using information from observations (have had limited success)  Night-side background level  Less well known than day-side  Use as fudge factor Solar Noon 45° Auroral Background

10.  1 st working with the two data sets separately – large disagreement  Likely due to errors & biases in the data & errors in conductance model FESD-ECCWES Meeting – 10 Feb 201410/13 Assimilative Mapping Examples SuperDARN AMPERE Σ bgd = 0.3 Σ bgd = 3 Φ J || AMPERE SuperDARN  More agreement if night-side conductance inflated to 3

11.  Solving with both data sets simultaneously FESD-ECCWES Meeting – 10 Feb 201411/13 Assimilative Mapping Examples J || ΣpΣp Φ

12. FESD-ECCWES Meeting – 10 Feb 201412/13 Assimilative Mapping Examples BYBZBYBZ AMPERE SuperDARN

13. FESD-ECCWES Meeting – 10 Feb 201413/13 Next Steps  Validation, refinement of procedure by comparing mapped results to independent observations  Have begun testing against subset of SuperDARN or AMPERE data excluded from fit  Look at geomagnetic disturbance within the week-long AMPERE data set